site selection for services (regression review for site selection in back) chapter 14
TRANSCRIPT
Site Selection for Services (Regression Review for site selection in back)
Chapter 14
Type of Service
• Quasi-Manufacturing – Goal - minimize logistics cost of a network– Examples - warehouses, call centers
• Delivered – Goal - covering a geographic area– Examples -
• Public Sector - fire protection, emergency medicine• Private Sector - food delivery, saturation strategy
Chapter 14 – Site Selection
Type of Service • Demand Sensitive
– Goal - attract customers through location– Examples - banks, restaurants
Academic Challenge:– Turn “gut feel” into science
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Demand Sensitive Service Facility Location • Use location to generate demand• Managerial Challenge: Forecasting
demand for specific locations• General Marketing/Operations Strategies• Site Specific Considerations
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Demand Sensitive Services
• Solution Techniques:– Informal judgment– Factor Rating– Regression
• Case:– La Quinta Hotels - Regression based site
selection
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Characteristics of a Good Location• Proximity to target market
– Residences, hospitals, schools, offices, airports, military bases
• Proximity to destination points– Malls tourist attractions, anchor stores
• Ease of access• Proximity to competition• Proximity to other units of the same type
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Problem: accurate identification and trade-offs
Demand Sensitive Service Facility Location
Factor Rating example
Item RangeIncome of neighborhood 0-40Proximity to shopping centers 0-25Accessibility 0-15Visibility 0-10Traffic 0-10
OR…
Chapter 14 – Site Selection
Demand Sensitive Service Facility Location
Factor Rating example
Item Scale MultiplierIncome of neighborhood 0-10 .40Proximity to shopping centers 0-10 .25Accessibility 0-10 .15Visibility 0-10 .10Traffic 0-10 .10
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Demand Sensitive Service Facility Location
Springfield 3.15
Tyson's Corner 8.00
Gaithersburg 9.20
Alexandria 5.10
Springfield
Tyson's Corner
Gaithersburg Alexandria
Income 4 8 10 6
Shopping 2 7 10 4
Access 1 9 8 4
Visibility 6 9 7 6
Traffic 3 8 8 5
Score
Factor Rating Example
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Demand Sensitive Service Facility Location • Regression Based - find variable
weightings from previous locations• La Quinta Case
─ Develop regression model for prior hotels─ Apply model results to a new site
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REGRESSION REVIEW • Variable selection - Theory First• Data types
– Ratio – Ordinal– Categorical
• Transforming variables• Outliers• Relevance of seemingly irrelevant variables
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Data Types • Ratio
– Ratios are meaningful: 6 apples are twice as good as 3 apples
• Ordinal– Implies better or worse, but ratios are not
meaningful: private=1, corporal=2, ... general=15
• Categorical– Coded categories, 2 is not better than 1. 1 if
red, 2 if blue, 3 if green
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Regression with Categorical DataColor Code SalesPink 1 42Pink 1 61Orange 3 24Orange 3 15Pink 1 38Pink 1 8Orange 3 63Green 2 64Green 2 68Green 2 33Orange 3 32Pink 1 60Green 2 10Orange 3 11Green 2 40Pink 1 7Green 2 57Green 2 15Pink 1 14Green 2 53Green 2 16
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Exploratory Data Analysis• Finding relationships
─ Mean/variance─ Scatter plots─ Correlation matrix (regular and transformed
variables)
• Outliers
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Scatter DiagramScatter Diagram
0102030405060708090
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0 5 10 15 20 25 30 35
Advertising Expenditures
Sa
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Regression LineRegression Line
0
1020
3040
50
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0 5 10 15 20 25 30 35
Advertising Expenditures
Sal
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Regression Line w/ Typo (outlier)Regression Line (Typo in Data)
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0 5 10 15 20 25 30 35
Advertising Expenditures
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Transforming Variables: Customers Visiting a Restaurant and Distance From the Workplace
Necessary but Irrelevant Variables
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Geographic Information Systems (GIS)• Purpose:
– Predict demand based on geographic databases
• Other uses– Sales territory partitioning – Vehicle routing – Politics– Geography– Biologists– Environmentalists
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Geographic Information Systems (GIS)• Size: $6Billion
• Vendors: ESRI, Tactician, Intergraph, GDS, Strategic Mapping, Mapinfo
• Users (ESRI): Ace Hardware, Anheuser Busch, Arby’s, AT&T, Avis, Banc One, BellSouth, Blockbuster, Chemical Bank, Chevron, Coca-cola, Dayton-Hudson…
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GIS Example – MapScape Report Choice
GIS Example – Map of Area Within ¼ Mile
Demographic Information of Area Within ¼ Mile
Map of Area Within Three Minute Drive
Demographic Information of Area Within Three Minute Drive
Delivered Services Facility Location • Criteria:
– Minimize costs of multiple sites that meet a service goal (e.g., everyone within a city boundary should be reached by ambulance within 15 minutes)
– OR, serve a maximum number of customers
• "Set Covering" Problem• Managerial Decisions:
− How many facilities− Location of facilities
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Delivered Services Facility Location• Procedure:
– Establish service goal– List potential sites or mathematically represent
service area– Determine demand from service area– Determine relationship of sites to demand
• (yes or no decision, can site i meet demand at point j considering established service goal)
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Example Problem for Delivered Services
Optimal Solution (linear programming)• Minimize Loc1 + Loc2 + Loc3 +…
{minimize the number of locations}s.t.
• Loc1 + Loc2 + Loc3 + Loc4 >=1 {Customer group 1 can only be served within the time frame by locations 1-4.}
• Loc1 + Loc2 + Loc3 >=1 {Customer group 2 can only be served by locations 1-3.}…
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Delivered Services - What Marketing Can Expect of Operations • Problems discussed:
– Covering area with a set of locations• Ex.: Rural ambulance problem
– Need for a plan• Ex.: Upscale service in Atlanta, locate in Buckhead
or Preston Hollow?
• Advanced Problems:– Planning Backup
• primary service in 5 min., backup in 10• Mobile Services - continuous dispatching
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Quasi-Manufacturing Service Facility Location
• Criteria: logistics cost minimization of multi-echelon system– Example: Stuff Products, Inc.
• Stuff Products has customers across the country and warehouses in New York, Chicago and Los Angeles. Below is a table of the costs of shipping a truck of Stuff from each warehouse to each demand point and the total demand at each point.
Philadelphia
Buffalo Baltimore Minneapolis Cleveland S.F.
New York 50 70 70 200 150 500
Chicago 200 200 250 100 50 300
L.A. 350 350 350 300 300 100
Demand 10 15 15 15 15 30
Formulate a linear program to determine the least cost solution to satisfy demand. Also, determine the best solution by hand (where “solution” means who should be served from which warehouse, not the total cost of the solution).
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Quasi-Manufacturing Service Facility Location • Example: Stuff Products, Inc.: The Sequel
– Stuff Products has customers across the country and wants to know where to build warehouses. They have identified sites in New York, Chicago and Los Angeles. Each warehouse costs $X to maintain per year.
Phil Buffalo Baltimore
Minn Cleve
S.F. Capacity
New York 50 70 70 200 150 500 50
Chicago 200 200 250 100 50 300 50
L.A. 350 350 350 300 300 100 50
Demand 10 15 15 15 15 30
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Quasi-Manufacturing Service Facility Location • Meta-problem of "Transportation" linear
programming problem• Managerial Decisions:
− How many facilities− Location of facilities− Customer assignment to facilities− Staffing/Capacity of each facility− Location decisions reviewed frequently
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Quasi-Manufacturing Service Facility Location • Commercial Software
– At least 16 vendors– Price $5,000 - $80,000– Solution Techniques
• Heuristics• Deterministic simulation• Mixed integer linear programming
– Limitations• Models handle small list of potential sites• No model provides optimal solutions
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Quasi-Manufacturing Service Facility Location• Mixed Integer Linear Programming
− Some variables must be integers, others can be fractions
− Constants• C - cost of serving demand point j with facility i• K - cost of building/maintaining facility i
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Quasi-Manufacturing Facility Location
Variables:X how much from each facility i to each demand
point jY = 1 if build facility, 0 if not
Minimize Costs: ∑i ∑j Cij Xij + ∑KiYk
s.t.
∑i Xij > Demand at point j
∑j Xij < Capacity at point i x Yj
Yj Є {0,1}
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Quasi-Manufacturing Facility Location
• Example: AT&T 800 Service Location Decisions for Call Centers– Criteria: minimization of telephone, labor, and
real estate costs– Old days: Omaha – the 800 capital of the
world– Today: Multiple sites, unusual telephone rate
structures (e.g., site in Tennessee may not take calls from within Tennessee)
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Quasi-Manufacturing Facility Location
• Example: AT&T 800 Service• Model: Mixed integer linear program• Client Range
– 46 clients in 1988 – retail catalogue, banking, consumer products, etc.
– 1-20 sites– Sites with 30-500 personnel
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